A Context-aware Convention Formation Framework for Large-Scale Networks

Conventions can serve as a useful mechanism for deciding the dominant coordination strategy and facilitating consensus in a multiagent system (MAS). In this paper, we present a decentralized convention formation framework that harnesses the structural properties and diversity of the network for creating social conventions within large and open multiagent convention spaces. We validate our convention formation framework using a language coordination problem in which agents in a MAS construct a common lexicon in a decentralized fashion on various networks. Experimentation results indicate that our approach is both effective (able to converge into a large majority convention state with more than 90% agents sharing a high-quality lexicon) and efficient (faster) as compared to state-of-the-art approaches for social conventions in large convention spaces.